{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乐学偶得版权所有 lexueoude.com 公众号：乐学Fintech"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果要进行计算BBand指标，必须要知道两个信息：\n",
    "MA + Kσ\n",
    "MA − Kσ\n",
    "\n",
    "1）MA（移动平均，很多时候我们用的是SMA）\n",
    "2）σ标准差(移动平均的标准差)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190628</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.79</td>\n",
       "      <td>13.58</td>\n",
       "      <td>13.78</td>\n",
       "      <td>13.71</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>498093.69</td>\n",
       "      <td>682679.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190627</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.85</td>\n",
       "      <td>13.45</td>\n",
       "      <td>13.71</td>\n",
       "      <td>13.37</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5430</td>\n",
       "      <td>925074.94</td>\n",
       "      <td>1270042.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190626</td>\n",
       "      <td>13.27</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.6020</td>\n",
       "      <td>546504.76</td>\n",
       "      <td>731207.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190625</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.07</td>\n",
       "      <td>13.43</td>\n",
       "      <td>13.69</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.8992</td>\n",
       "      <td>1469227.07</td>\n",
       "      <td>1954855.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190624</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.83</td>\n",
       "      <td>13.61</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.64</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3666</td>\n",
       "      <td>659572.85</td>\n",
       "      <td>904433.349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0  000001.SZ   20190628  13.73  13.79  13.58  13.78      13.71    0.07   \n",
       "1  000001.SZ   20190627  13.50  13.85  13.45  13.71      13.37    0.34   \n",
       "2  000001.SZ   20190626  13.27  13.50  13.19  13.37      13.29    0.08   \n",
       "3  000001.SZ   20190625  13.72  13.72  13.07  13.43      13.69   -0.26   \n",
       "4  000001.SZ   20190624  13.69  13.83  13.61  13.69      13.64    0.05   \n",
       "\n",
       "   pct_chg         vol       amount  \n",
       "0   0.5106   498093.69   682679.970  \n",
       "1   2.5430   925074.94  1270042.461  \n",
       "2   0.6020   546504.76   731207.282  \n",
       "3  -1.8992  1469227.07  1954855.785  \n",
       "4   0.3666   659572.85   904433.349  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "pro=ts.pro_api()\n",
    "df_whole=pro.daily(ts_code=\"000001.SZ\",start_date=\"20170101\",end_date=\"20190630\")\n",
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2019-06-28\n",
       "1     2019-06-27\n",
       "2     2019-06-26\n",
       "3     2019-06-25\n",
       "4     2019-06-24\n",
       "5     2019-06-21\n",
       "6     2019-06-20\n",
       "7     2019-06-19\n",
       "8     2019-06-18\n",
       "9     2019-06-17\n",
       "10    2019-06-14\n",
       "11    2019-06-13\n",
       "12    2019-06-12\n",
       "13    2019-06-11\n",
       "14    2019-06-10\n",
       "15    2019-06-06\n",
       "16    2019-06-05\n",
       "17    2019-06-04\n",
       "18    2019-06-03\n",
       "19    2019-05-31\n",
       "20    2019-05-30\n",
       "21    2019-05-29\n",
       "22    2019-05-28\n",
       "23    2019-05-27\n",
       "24    2019-05-24\n",
       "25    2019-05-23\n",
       "26    2019-05-22\n",
       "27    2019-05-21\n",
       "28    2019-05-20\n",
       "29    2019-05-17\n",
       "         ...    \n",
       "575   2017-02-20\n",
       "576   2017-02-17\n",
       "577   2017-02-16\n",
       "578   2017-02-15\n",
       "579   2017-02-14\n",
       "580   2017-02-13\n",
       "581   2017-02-10\n",
       "582   2017-02-09\n",
       "583   2017-02-08\n",
       "584   2017-02-07\n",
       "585   2017-02-06\n",
       "586   2017-02-03\n",
       "587   2017-01-26\n",
       "588   2017-01-25\n",
       "589   2017-01-24\n",
       "590   2017-01-23\n",
       "591   2017-01-20\n",
       "592   2017-01-19\n",
       "593   2017-01-18\n",
       "594   2017-01-17\n",
       "595   2017-01-16\n",
       "596   2017-01-13\n",
       "597   2017-01-12\n",
       "598   2017-01-11\n",
       "599   2017-01-10\n",
       "600   2017-01-09\n",
       "601   2017-01-06\n",
       "602   2017-01-05\n",
       "603   2017-01-04\n",
       "604   2017-01-03\n",
       "Name: trade_date, Length: 605, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole[\"trade_date\"]=pd.to_datetime(df_whole[\"trade_date\"],format=\"%Y%m%d\")\n",
    "df_whole[\"trade_date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.79</td>\n",
       "      <td>13.58</td>\n",
       "      <td>13.78</td>\n",
       "      <td>13.71</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>498093.69</td>\n",
       "      <td>682679.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.85</td>\n",
       "      <td>13.45</td>\n",
       "      <td>13.71</td>\n",
       "      <td>13.37</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5430</td>\n",
       "      <td>925074.94</td>\n",
       "      <td>1270042.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.27</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.6020</td>\n",
       "      <td>546504.76</td>\n",
       "      <td>731207.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.07</td>\n",
       "      <td>13.43</td>\n",
       "      <td>13.69</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.8992</td>\n",
       "      <td>1469227.07</td>\n",
       "      <td>1954855.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.83</td>\n",
       "      <td>13.61</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.64</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3666</td>\n",
       "      <td>659572.85</td>\n",
       "      <td>904433.349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2019-06-28  000001.SZ  13.73  13.79  13.58  13.78      13.71    0.07   0.5106   \n",
       "2019-06-27  000001.SZ  13.50  13.85  13.45  13.71      13.37    0.34   2.5430   \n",
       "2019-06-26  000001.SZ  13.27  13.50  13.19  13.37      13.29    0.08   0.6020   \n",
       "2019-06-25  000001.SZ  13.72  13.72  13.07  13.43      13.69   -0.26  -1.8992   \n",
       "2019-06-24  000001.SZ  13.69  13.83  13.61  13.69      13.64    0.05   0.3666   \n",
       "\n",
       "                   vol       amount  \n",
       "trade_date                           \n",
       "2019-06-28   498093.69   682679.970  \n",
       "2019-06-27   925074.94  1270042.461  \n",
       "2019-06-26   546504.76   731207.282  \n",
       "2019-06-25  1469227.07  1954855.785  \n",
       "2019-06-24   659572.85   904433.349  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole.set_index(\"trade_date\",inplace=True)\n",
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.11</td>\n",
       "      <td>9.18</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.16</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.66</td>\n",
       "      <td>459840.49</td>\n",
       "      <td>420595.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.15</td>\n",
       "      <td>9.18</td>\n",
       "      <td>9.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>9.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>449329.53</td>\n",
       "      <td>411503.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.18</td>\n",
       "      <td>9.15</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.11</td>\n",
       "      <td>344372.91</td>\n",
       "      <td>315769.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.11</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.17</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>358154.20</td>\n",
       "      <td>327176.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.11</td>\n",
       "      <td>9.15</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.22</td>\n",
       "      <td>361081.57</td>\n",
       "      <td>329994.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code  open  high   low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                   \n",
       "2017-01-03  000001.SZ  9.11  9.18  9.09   9.16       9.10    0.06     0.66   \n",
       "2017-01-04  000001.SZ  9.15  9.18  9.14   9.16       9.16    0.00     0.00   \n",
       "2017-01-05  000001.SZ  9.17  9.18  9.15   9.17       9.16    0.01     0.11   \n",
       "2017-01-06  000001.SZ  9.17  9.17  9.11   9.13       9.17   -0.04    -0.44   \n",
       "2017-01-09  000001.SZ  9.13  9.17  9.11   9.15       9.13    0.02     0.22   \n",
       "\n",
       "                  vol      amount  \n",
       "trade_date                         \n",
       "2017-01-03  459840.49  420595.176  \n",
       "2017-01-04  449329.53  411503.444  \n",
       "2017-01-05  344372.91  315769.693  \n",
       "2017-01-06  358154.20  327176.433  \n",
       "2017-01-09  361081.57  329994.604  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole=df_whole.sort_index(ascending=True)\n",
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2017-01-03    9.16\n",
       "2017-01-04    9.16\n",
       "2017-01-05    9.17\n",
       "2017-01-06    9.13\n",
       "2017-01-09    9.15\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbol=\"000001.SZ\"\n",
    "df=df_whole[\"close\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2017-01-03        NaN\n",
       "2017-01-04        NaN\n",
       "2017-01-05        NaN\n",
       "2017-01-06        NaN\n",
       "2017-01-09        NaN\n",
       "2017-01-10        NaN\n",
       "2017-01-11        NaN\n",
       "2017-01-12        NaN\n",
       "2017-01-13        NaN\n",
       "2017-01-16        NaN\n",
       "2017-01-17        NaN\n",
       "2017-01-18        NaN\n",
       "2017-01-19        NaN\n",
       "2017-01-20        NaN\n",
       "2017-01-23        NaN\n",
       "2017-01-24        NaN\n",
       "2017-01-25        NaN\n",
       "2017-01-26        NaN\n",
       "2017-02-03        NaN\n",
       "2017-02-06     9.1940\n",
       "2017-02-07     9.2010\n",
       "2017-02-08     9.2080\n",
       "2017-02-09     9.2150\n",
       "2017-02-10     9.2250\n",
       "2017-02-13     9.2380\n",
       "2017-02-14     9.2505\n",
       "2017-02-15     9.2660\n",
       "2017-02-16     9.2815\n",
       "2017-02-17     9.2930\n",
       "2017-02-20     9.3140\n",
       "               ...   \n",
       "2019-05-17    13.4105\n",
       "2019-05-20    13.3120\n",
       "2019-05-21    13.2230\n",
       "2019-05-22    13.1065\n",
       "2019-05-23    13.0135\n",
       "2019-05-24    12.9275\n",
       "2019-05-27    12.8240\n",
       "2019-05-28    12.7420\n",
       "2019-05-29    12.6725\n",
       "2019-05-30    12.5785\n",
       "2019-05-31    12.4950\n",
       "2019-06-03    12.4465\n",
       "2019-06-04    12.3915\n",
       "2019-06-05    12.3600\n",
       "2019-06-06    12.3480\n",
       "2019-06-10    12.3310\n",
       "2019-06-11    12.3485\n",
       "2019-06-12    12.3525\n",
       "2019-06-13    12.3360\n",
       "2019-06-14    12.3180\n",
       "2019-06-17    12.3295\n",
       "2019-06-18    12.3505\n",
       "2019-06-19    12.3760\n",
       "2019-06-20    12.4460\n",
       "2019-06-21    12.5135\n",
       "2019-06-24    12.5805\n",
       "2019-06-25    12.6335\n",
       "2019-06-26    12.6775\n",
       "2019-06-27    12.7430\n",
       "2019-06-28    12.8210\n",
       "Name: close, Length: 605, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma=df.rolling(window=20).mean()\n",
    "sma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2017-01-03         NaN\n",
       "2017-01-04         NaN\n",
       "2017-01-05         NaN\n",
       "2017-01-06         NaN\n",
       "2017-01-09         NaN\n",
       "2017-01-10         NaN\n",
       "2017-01-11         NaN\n",
       "2017-01-12         NaN\n",
       "2017-01-13         NaN\n",
       "2017-01-16         NaN\n",
       "2017-01-17         NaN\n",
       "2017-01-18         NaN\n",
       "2017-01-19         NaN\n",
       "2017-01-20         NaN\n",
       "2017-01-23         NaN\n",
       "2017-01-24         NaN\n",
       "2017-01-25         NaN\n",
       "2017-01-26         NaN\n",
       "2017-02-03         NaN\n",
       "2017-02-06    0.060819\n",
       "2017-02-07    0.064637\n",
       "2017-02-08    0.067481\n",
       "2017-02-09    0.070524\n",
       "2017-02-10    0.072001\n",
       "2017-02-13    0.080694\n",
       "2017-02-14    0.085562\n",
       "2017-02-15    0.092304\n",
       "2017-02-16    0.097672\n",
       "2017-02-17    0.096141\n",
       "2017-02-20    0.106297\n",
       "                ...   \n",
       "2019-05-17    0.849002\n",
       "2019-05-20    0.848544\n",
       "2019-05-21    0.828150\n",
       "2019-05-22    0.766594\n",
       "2019-05-23    0.745882\n",
       "2019-05-24    0.716225\n",
       "2019-05-27    0.630600\n",
       "2019-05-28    0.553787\n",
       "2019-05-29    0.499946\n",
       "2019-05-30    0.379699\n",
       "2019-05-31    0.245153\n",
       "2019-06-03    0.262403\n",
       "2019-06-04    0.266562\n",
       "2019-06-05    0.277621\n",
       "2019-06-06    0.291558\n",
       "2019-06-10    0.280899\n",
       "2019-06-11    0.289633\n",
       "2019-06-12    0.292231\n",
       "2019-06-13    0.266703\n",
       "2019-06-14    0.241107\n",
       "2019-06-17    0.252451\n",
       "2019-06-18    0.273467\n",
       "2019-06-19    0.314700\n",
       "2019-06-20    0.447853\n",
       "2019-06-21    0.519162\n",
       "2019-06-24    0.579868\n",
       "2019-06-25    0.607404\n",
       "2019-06-26    0.627987\n",
       "2019-06-27    0.664760\n",
       "2019-06-28    0.691161\n",
       "Name: close, Length: 605, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rolling standard deviation\n",
    "rstd=df.rolling(window=20).std()\n",
    "rstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2017-01-03          NaN\n",
       "2017-01-04          NaN\n",
       "2017-01-05          NaN\n",
       "2017-01-06          NaN\n",
       "2017-01-09          NaN\n",
       "2017-01-10          NaN\n",
       "2017-01-11          NaN\n",
       "2017-01-12          NaN\n",
       "2017-01-13          NaN\n",
       "2017-01-16          NaN\n",
       "2017-01-17          NaN\n",
       "2017-01-18          NaN\n",
       "2017-01-19          NaN\n",
       "2017-01-20          NaN\n",
       "2017-01-23          NaN\n",
       "2017-01-24          NaN\n",
       "2017-01-25          NaN\n",
       "2017-01-26          NaN\n",
       "2017-02-03          NaN\n",
       "2017-02-06     9.315638\n",
       "2017-02-07     9.330273\n",
       "2017-02-08     9.342962\n",
       "2017-02-09     9.356049\n",
       "2017-02-10     9.369003\n",
       "2017-02-13     9.399389\n",
       "2017-02-14     9.421623\n",
       "2017-02-15     9.450608\n",
       "2017-02-16     9.476843\n",
       "2017-02-17     9.485283\n",
       "2017-02-20     9.526593\n",
       "                ...    \n",
       "2019-05-17    15.108505\n",
       "2019-05-20    15.009089\n",
       "2019-05-21    14.879300\n",
       "2019-05-22    14.639688\n",
       "2019-05-23    14.505264\n",
       "2019-05-24    14.359949\n",
       "2019-05-27    14.085201\n",
       "2019-05-28    13.849574\n",
       "2019-05-29    13.672392\n",
       "2019-05-30    13.337898\n",
       "2019-05-31    12.985306\n",
       "2019-06-03    12.971307\n",
       "2019-06-04    12.924625\n",
       "2019-06-05    12.915243\n",
       "2019-06-06    12.931117\n",
       "2019-06-10    12.892798\n",
       "2019-06-11    12.927765\n",
       "2019-06-12    12.936961\n",
       "2019-06-13    12.869406\n",
       "2019-06-14    12.800214\n",
       "2019-06-17    12.834401\n",
       "2019-06-18    12.897433\n",
       "2019-06-19    13.005399\n",
       "2019-06-20    13.341707\n",
       "2019-06-21    13.551824\n",
       "2019-06-24    13.740236\n",
       "2019-06-25    13.848308\n",
       "2019-06-26    13.933473\n",
       "2019-06-27    14.072521\n",
       "2019-06-28    14.203323\n",
       "Name: close, Length: 605, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_band=sma+ 2 * rstd\n",
    "upper_band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(upper_band)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_band=upper_band.to_frame()\n",
    "type(upper_band)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>9.315638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>9.330273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>9.342962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>9.356049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>9.369003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>9.399389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>9.421623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-15</th>\n",
       "      <td>9.450608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>9.476843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-17</th>\n",
       "      <td>9.485283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <td>9.526593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-17</th>\n",
       "      <td>15.108505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-20</th>\n",
       "      <td>15.009089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21</th>\n",
       "      <td>14.879300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-22</th>\n",
       "      <td>14.639688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-23</th>\n",
       "      <td>14.505264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-24</th>\n",
       "      <td>14.359949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>14.085201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28</th>\n",
       "      <td>13.849574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29</th>\n",
       "      <td>13.672392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30</th>\n",
       "      <td>13.337898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>12.985306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-03</th>\n",
       "      <td>12.971307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>12.924625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>12.915243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>12.931117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>12.892798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>12.927765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>12.936961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>12.869406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>12.800214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17</th>\n",
       "      <td>12.834401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-18</th>\n",
       "      <td>12.897433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-19</th>\n",
       "      <td>13.005399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>13.341707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21</th>\n",
       "      <td>13.551824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>13.740236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>13.848308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>13.933473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>14.072521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>14.203323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>605 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                close\n",
       "trade_date           \n",
       "2017-01-03        NaN\n",
       "2017-01-04        NaN\n",
       "2017-01-05        NaN\n",
       "2017-01-06        NaN\n",
       "2017-01-09        NaN\n",
       "2017-01-10        NaN\n",
       "2017-01-11        NaN\n",
       "2017-01-12        NaN\n",
       "2017-01-13        NaN\n",
       "2017-01-16        NaN\n",
       "2017-01-17        NaN\n",
       "2017-01-18        NaN\n",
       "2017-01-19        NaN\n",
       "2017-01-20        NaN\n",
       "2017-01-23        NaN\n",
       "2017-01-24        NaN\n",
       "2017-01-25        NaN\n",
       "2017-01-26        NaN\n",
       "2017-02-03        NaN\n",
       "2017-02-06   9.315638\n",
       "2017-02-07   9.330273\n",
       "2017-02-08   9.342962\n",
       "2017-02-09   9.356049\n",
       "2017-02-10   9.369003\n",
       "2017-02-13   9.399389\n",
       "2017-02-14   9.421623\n",
       "2017-02-15   9.450608\n",
       "2017-02-16   9.476843\n",
       "2017-02-17   9.485283\n",
       "2017-02-20   9.526593\n",
       "...               ...\n",
       "2019-05-17  15.108505\n",
       "2019-05-20  15.009089\n",
       "2019-05-21  14.879300\n",
       "2019-05-22  14.639688\n",
       "2019-05-23  14.505264\n",
       "2019-05-24  14.359949\n",
       "2019-05-27  14.085201\n",
       "2019-05-28  13.849574\n",
       "2019-05-29  13.672392\n",
       "2019-05-30  13.337898\n",
       "2019-05-31  12.985306\n",
       "2019-06-03  12.971307\n",
       "2019-06-04  12.924625\n",
       "2019-06-05  12.915243\n",
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       "2019-06-27  14.072521\n",
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       "[605 rows x 1 columns]"
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     "execution_count": 11,
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>11.769235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>11.768039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>11.802594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>11.835786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17</th>\n",
       "      <td>11.824599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-18</th>\n",
       "      <td>11.803567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-19</th>\n",
       "      <td>11.746601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>11.550293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21</th>\n",
       "      <td>11.475176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>11.420764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>11.418692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>11.421527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>11.413479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>11.438677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>605 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                lower\n",
       "trade_date           \n",
       "2017-01-03        NaN\n",
       "2017-01-04        NaN\n",
       "2017-01-05        NaN\n",
       "2017-01-06        NaN\n",
       "2017-01-09        NaN\n",
       "2017-01-10        NaN\n",
       "2017-01-11        NaN\n",
       "2017-01-12        NaN\n",
       "2017-01-13        NaN\n",
       "2017-01-16        NaN\n",
       "2017-01-17        NaN\n",
       "2017-01-18        NaN\n",
       "2017-01-19        NaN\n",
       "2017-01-20        NaN\n",
       "2017-01-23        NaN\n",
       "2017-01-24        NaN\n",
       "2017-01-25        NaN\n",
       "2017-01-26        NaN\n",
       "2017-02-03        NaN\n",
       "2017-02-06   9.072362\n",
       "2017-02-07   9.071727\n",
       "2017-02-08   9.073038\n",
       "2017-02-09   9.073951\n",
       "2017-02-10   9.080997\n",
       "2017-02-13   9.076611\n",
       "2017-02-14   9.079377\n",
       "2017-02-15   9.081392\n",
       "2017-02-16   9.086157\n",
       "2017-02-17   9.100717\n",
       "2017-02-20   9.101407\n",
       "...               ...\n",
       "2019-05-17  11.712495\n",
       "2019-05-20  11.614911\n",
       "2019-05-21  11.566700\n",
       "2019-05-22  11.573312\n",
       "2019-05-23  11.521736\n",
       "2019-05-24  11.495051\n",
       "2019-05-27  11.562799\n",
       "2019-05-28  11.634426\n",
       "2019-05-29  11.672608\n",
       "2019-05-30  11.819102\n",
       "2019-05-31  12.004694\n",
       "2019-06-03  11.921693\n",
       "2019-06-04  11.858375\n",
       "2019-06-05  11.804757\n",
       "2019-06-06  11.764883\n",
       "2019-06-10  11.769202\n",
       "2019-06-11  11.769235\n",
       "2019-06-12  11.768039\n",
       "2019-06-13  11.802594\n",
       "2019-06-14  11.835786\n",
       "2019-06-17  11.824599\n",
       "2019-06-18  11.803567\n",
       "2019-06-19  11.746601\n",
       "2019-06-20  11.550293\n",
       "2019-06-21  11.475176\n",
       "2019-06-24  11.420764\n",
       "2019-06-25  11.418692\n",
       "2019-06-26  11.421527\n",
       "2019-06-27  11.413479\n",
       "2019-06-28  11.438677\n",
       "\n",
       "[605 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_band=sma- 2 * rstd\n",
    "lower_band=lower_band.to_frame()\n",
    "lower_band=lower_band.rename({\"close\":\"lower\"},axis=1)\n",
    "lower_band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            close      upper      lower\n",
      "trade_date                             \n",
      "2017-02-06   9.31   9.315638   9.072362\n",
      "2017-02-07   9.30   9.330273   9.071727\n",
      "2017-02-08   9.30   9.342962   9.073038\n",
      "2017-02-09   9.31   9.356049   9.073951\n",
      "2017-02-10   9.33   9.369003   9.080997\n",
      "2017-02-13   9.41   9.399389   9.076611\n",
      "2017-02-14   9.40   9.421623   9.079377\n",
      "2017-02-15   9.45   9.450608   9.081392\n",
      "2017-02-16   9.46   9.476843   9.086157\n",
      "2017-02-17   9.39   9.485283   9.100717\n",
      "2017-02-20   9.56   9.526593   9.101407\n",
      "2017-02-21   9.57   9.561878   9.108122\n",
      "2017-02-22   9.57   9.590975   9.119025\n",
      "2017-02-23   9.51   9.602039   9.140961\n",
      "2017-02-24   9.50   9.611260   9.159740\n",
      "2017-02-27   9.43   9.608494   9.183506\n",
      "2017-02-28   9.48   9.613460   9.199540\n",
      "2017-03-01   9.49   9.616054   9.219946\n",
      "2017-03-02   9.43   9.616701   9.229299\n",
      "2017-03-03   9.40   9.608414   9.251586\n",
      "2017-03-06   9.45   9.606345   9.267655\n",
      "2017-03-07   9.45   9.601105   9.287895\n",
      "2017-03-08   9.42   9.592289   9.308711\n",
      "2017-03-09   9.38   9.584166   9.323834\n",
      "2017-03-10   9.40   9.576950   9.338050\n",
      "2017-03-13   9.44   9.576679   9.341321\n",
      "2017-03-14   9.44   9.575781   9.346219\n",
      "2017-03-15   9.48   9.577460   9.347540\n",
      "2017-03-16   9.52   9.583282   9.347718\n",
      "2017-03-17   9.31   9.594526   9.328474\n",
      "...           ...        ...        ...\n",
      "2019-05-17  12.44  15.108505  11.712495\n",
      "2019-05-20  12.38  15.009089  11.614911\n",
      "2019-05-21  12.56  14.879300  11.566700\n",
      "2019-05-22  12.40  14.639688  11.573312\n",
      "2019-05-23  12.29  14.505264  11.521736\n",
      "2019-05-24  12.35  14.359949  11.495051\n",
      "2019-05-27  12.37  14.085201  11.562799\n",
      "2019-05-28  12.49  13.849574  11.634426\n",
      "2019-05-29  12.40  13.672392  11.672608\n",
      "2019-05-30  12.22  13.337898  11.819102\n",
      "2019-05-31  12.18  12.985306  12.004694\n",
      "2019-06-03  11.90  12.971307  11.921693\n",
      "2019-06-04  11.85  12.924625  11.858375\n",
      "2019-06-05  11.97  12.915243  11.804757\n",
      "2019-06-06  11.92  12.931117  11.764883\n",
      "2019-06-10  12.34  12.892798  11.769202\n",
      "2019-06-11  12.65  12.927765  11.769235\n",
      "2019-06-12  12.57  12.936961  11.768039\n",
      "2019-06-13  12.59  12.869406  11.802594\n",
      "2019-06-14  12.49  12.800214  11.835786\n",
      "2019-06-17  12.67  12.834401  11.824599\n",
      "2019-06-18  12.80  12.897433  11.803567\n",
      "2019-06-19  13.07  13.005399  11.746601\n",
      "2019-06-20  13.80  13.341707  11.550293\n",
      "2019-06-21  13.64  13.551824  11.475176\n",
      "2019-06-24  13.69  13.740236  11.420764\n",
      "2019-06-25  13.43  13.848308  11.418692\n",
      "2019-06-26  13.37  13.933473  11.421527\n",
      "2019-06-27  13.71  14.072521  11.413479\n",
      "2019-06-28  13.78  14.203323  11.438677\n",
      "\n",
      "[586 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "BB_df=pd.concat([df,upper_band,lower_band],axis=1)\n",
    "BB_df.dropna(inplace=True)\n",
    "print(BB_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=BB_df.plot(title=\"{} Price and Bollinger Band\".format(symbol),legend=True)\n",
    "ax.fill_between(BB_df.index,BB_df[\"lower\"],BB_df[\"upper\"],color=\"#d9b3ff\",alpha=\"0.2\")\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"Price(SMA and Bollinger Band )\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
